AI-driven capacity forecasting and planning for microservices apps

ABSTRACT

In one embodiment, a resource allocation process determines a plurality of service levels of applications (e.g., business transactions) during a monitored period, and examines infrastructure performance data (utilization of a plurality of resources and a plurality of performance metrics) of a plurality of services in a microservices architecture in relation to each of the plurality of service levels of the applications. Accordingly, a resource capacity model can be generated for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of resources to satisfy specified performance metric constraints during operation of the applications at given service levels. As such, the resource allocation process can effectuate, based on the resource capacity model, a specific capacity of resources required for a particular time of operation of the applications at a particular service level.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to artificial intelligence (AI)-driven capacity forecasting and planning for microservices applications (apps).

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation of web services available for virtually all types of businesses. Due to the accompanying complexity of the infrastructure supporting the web services, it is becoming increasingly difficult to maintain the highest level of service performance and user experience to keep up with the increase in web services. For example, it can be challenging to piece together monitoring and logging data across disparate systems, tools, and layers in a network architecture. Moreover, even when data can be obtained, it is difficult to directly connect the chain of events and cause and effect.

In one particular example, service-oriented architecture (SOA) has developed over time from merely structuring an application as a collection of loosely coupled services into a highly dynamic “microservices” architecture. In a microservices architecture, applications (apps) are composed of small (e.g., fine-grained), independent modules that communicate with each other using well-defined application programming interfaces (APIs) (e.g., over a network) to fulfill an action. The benefit of decomposing an application into different smaller services is that it improves modularity, making the application easier to understand, develop, test, and troubleshoot. However, in today's highly dynamic microservices environments, it is difficult to ensure that there is sufficient capacity and redundancy for microservices apps to serve projected future demand with required availability (e.g., meeting service level agreements, or SLAs). Traditional manual methods of forecasting capacity for microservices apps are inadequate in these dynamic, high-growth environments.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for implementing the example application intelligence platform;

FIG. 5 illustrates an example computing system implementing the disclosed technology;

FIG. 6 illustrates an example computing environment where an application-aware capacity engine can take in a plurality of inputs in order to determine a capacity model;

FIG. 7 illustrates an example resource capacity model based on curves of performance metrics mapped against a service level; and

FIGS. 8A-8B illustrate an example simplified procedure for AI-driven capacity forecasting and planning for microservices apps in accordance with one or more embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a resource allocation process determines service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications (e.g., business transactions) within a particular computer network topology. Also, one or more specified performance metric constraints on operation of the plurality of applications may also be determined. Based on a monitored period, the resource allocation process determines a plurality of service levels of the plurality of applications, and examines infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, where the infrastructure performance data comprises utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels. Accordingly, a resource capacity model can be generated for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels. As such, the resource allocation process can effectuate, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications (e.g., business transactions) at a particular service level.

Other embodiments are described below, and this overview is not meant to limit the scope of the present disclosure.

DESCRIPTION

A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC), and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. Other types of networks, such as field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), enterprise networks, etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or power-line communication networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100 illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers 110 may be interconnected with provider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order to communicate across a core network, such as an illustrative network backbone 130. For example, routers 110, 120 may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets 140 (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network 100 over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail, according to various embodiments. As shown, network backbone 130 may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network 100 may comprise local/branch networks 160, 162 that include devices/nodes 10-16 and devices/nodes 18-20, respectively, as well as a data center/cloud environment 150 that includes servers 152-154. Notably, local networks 160-162 and data center/cloud environment 150 may be located in different geographic locations. Servers 152-154 may include, in various embodiments, any number of suitable servers or other cloud-based resources. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. Furthermore, in various embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for nodes/devices 10-16 in the local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device (e.g., apparatus) 200 that may be used with one or more embodiments described herein, e.g., as any of the devices shown in FIGS. 1A-1B above, and particularly as specific devices as described further below. The device may comprise one or more network interfaces 210 (e.g., wired, wireless, etc.), at least one processor 220, and a memory 240 interconnected by a system bus 250, as well as a power supply 260 (e.g., battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, and signaling circuitry for communicating data over links coupled to the network 100, e.g., providing a data connection between device 200 and the data network, such as the Internet. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. For example, interfaces 210 may include wired transceivers, wireless transceivers, cellular transceivers, or the like, each to allow device 200 to communicate information to and from a remote computing device or server over an appropriate network. The same network interfaces 210 also allow communities of multiple devices 200 to interconnect among themselves, either peer-to-peer, or up and down a hierarchy. Note, further, that the nodes may have two different types of network connections 210, e.g., wireless and wired/physical connections, and that the view herein is merely for illustration. Also, while the network interface 210 is shown separately from power supply 260, for devices using powerline communication (PLC) or Power over Ethernet (PoE), the network interface 210 may communicate through the power supply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that are addressable by the processor 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise hardware elements or hardware logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242, portions of which are typically resident in memory 240 and executed by the processor, functionally organizes the device by, among other things, invoking operations in support of software processes and/or services executing on the device. These software processes and/or services may comprise one or more functional processes 246, and on certain devices, an illustrative “resource allocation” process 248, as described herein. Notably, functional processes 246, when executed by processor(s) 220, cause each particular device 200 to perform the various functions corresponding to the particular device's purpose and general configuration. For example, a router would be configured to operate as a router, a server would be configured to operate as a server, an access point (or gateway) would be configured to operate as an access point (or gateway), a client device would be configured to operate as a client device, and so on.

It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while the processes have been shown separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

—Application Intelligence Platform—

The embodiments herein relate to an application intelligence platform for application performance management. In one aspect, as discussed with respect to FIGS. 3-5 below, performance within a networking environment may be monitored, specifically by monitoring applications and entities (e.g., transactions, tiers, nodes, and machines) in the networking environment using agents installed at individual machines at the entities. As an example, applications may be configured to run on one or more machines (e.g., a customer will typically run one or more nodes on a machine, where an application consists of one or more tiers, and a tier consists of one or more nodes). The agents collect data associated with the applications of interest and associated nodes and machines where the applications are being operated. Examples of the collected data may include performance data (e.g., metrics, metadata, etc.) and topology data (e.g., indicating relationship information). The agent-collected data may then be provided to one or more servers or controllers to analyze the data.

FIG. 3 is a block diagram of an example application intelligence platform 300 that can implement one or more aspects of the techniques herein. The application intelligence platform is a system that monitors and collects metrics of performance data for an application environment being monitored. At the simplest structure, the application intelligence platform includes one or more agents 310 and one or more servers/controllers 320. Note that while FIG. 3 shows four agents (e.g., Agent 1 through Agent 4) communicatively linked to a single controller, the total number of agents and controllers can vary based on a number of factors including the number of applications monitored, how distributed the application environment is, the level of monitoring desired, the level of user experience desired, and so on.

The controller 320 is the central processing and administration server for the application intelligence platform. The controller 320 serves a browser-based user interface (UI) 330 that is the primary interface for monitoring, analyzing, and troubleshooting the monitored environment. The controller 320 can control and manage monitoring of business transactions (described below) distributed over application servers. Specifically, the controller 320 can receive runtime data from agents 310 (and/or other coordinator devices), associate portions of business transaction data, communicate with agents to configure collection of runtime data, and provide performance data and reporting through the interface 330. The interface 330 may be viewed as a web-based interface viewable by a client device 340. In some implementations, a client device 340 can directly communicate with controller 320 to view an interface for monitoring data. The controller 320 can include a visualization system 350 for displaying the reports and dashboards related to the disclosed technology. In some implementations, the visualization system 350 can be implemented in a separate machine (e.g., a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation, a controller instance 320 may be hosted remotely by a provider of the application intelligence platform 300. In an illustrative on-premises (On-Prem) implementation, a controller instance 320 may be installed locally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents 1-4) deployed to monitor applications, databases and database servers, servers, and end user clients for the monitored environment. Any of the agents 310 can be implemented as different types of agents with specific monitoring duties. For example, application agents may be installed on each server that hosts applications to be monitored. Instrumenting an agent adds an application agent into the runtime process of the application.

Database agents, for example, may be software (e.g., a Java program) installed on a machine that has network access to the monitored databases and the controller. Database agents query the monitored databases in order to collect metrics and pass those metrics along for display in a metric browser (e.g., for database monitoring and analysis within databases pages of the controller's UI 330). Multiple database agents can report to the same controller. Additional database agents can be implemented as backup database agents to take over for the primary database agents during a failure or planned machine downtime. The additional database agents can run on the same machine as the primary agents or on different machines. A database agent can be deployed in each distinct network of the monitored environment. Multiple database agents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs (e.g., standalone Java programs) that collect hardware-related performance statistics from the servers (or other suitable devices) in the monitored environment. The standalone machine agents can be deployed on machines that host application servers, database servers, messaging servers, Web servers, etc. A standalone machine agent has an extensible architecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents and mobile agents to provide performance information from the point of view of the client, such as a web browser or a mobile native application. Through EUM, web use, mobile use, or combinations thereof (e.g., by real users or synthetic agents) can be monitored based on the monitoring needs. Notably, browser agents (e.g., agents 310) can include Reporters that report monitored data to the controller.

Monitoring through browser agents and mobile agents are generally unlike monitoring through application agents, database agents, and standalone machine agents that are on the server. In particular, browser agents may generally be embodied as small files using web-based technologies, such as JavaScript agents injected into each instrumented web page (e.g., as close to the top as possible) as the web page is served, and are configured to collect data. Once the web page has completed loading, the collected data may be bundled into a beacon and sent to an EUM process/cloud for processing and made ready for retrieval by the controller. Browser real user monitoring (Browser RUM) provides insights into the performance of a web application from the point of view of a real or synthetic end user. For example, Browser RUM can determine how specific Ajax or iframe calls are slowing down page load time and how server performance impact end user experience in aggregate or in individual cases.

A mobile agent, on the other hand, may be a small piece of highly performant code that gets added to the source of the mobile application. Mobile RUM provides information on the native mobile application (e.g., iOS or Android applications) as the end users actually use the mobile application. Mobile RUM provides visibility into the functioning of the mobile application itself and the mobile application's interaction with the network used and any server-side applications with which the mobile application communicates.

Application Intelligence Monitoring: The disclosed technology can provide application intelligence data by monitoring an application environment that includes various services such as web applications served from an application server (e.g., Java virtual machine (JVM), Internet Information Services (IIS), Hypertext Preprocessor (PHP) Web server, etc.), databases or other data stores, and remote services such as message queues and caches. The services in the application environment can interact in various ways to provide a set of cohesive user interactions with the application, such as a set of user services applicable to end user customers.

Application Intelligence Modeling: Entities in the application environment (such as the JBoss service, MQSeries modules, and databases) and the services provided by the entities (such as a login transaction, service or product search, or purchase transaction) may be mapped to an application intelligence model. In the application intelligence model, a business transaction represents a particular service provided by the monitored environment. For example, in an e-commerce application, particular real-world services can include a user logging in, searching for items, or adding items to the cart. In a content portal, particular real-world services can include user requests for content such as sports, business, or entertainment news. In a stock trading application, particular real-world services can include operations such as receiving a stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of the particular service provided by the monitored environment provides a view on performance data in the context of the various tiers that participate in processing a particular request. A business transaction, which may each be identified by a unique business transaction identification (ID), represents the end-to-end processing path used to fulfill a service request in the monitored environment (e.g., adding items to a shopping cart, storing information in a database, purchasing an item online, etc.). Thus, a business transaction is a type of user-initiated action in the monitored environment defined by an entry point and a processing path across application servers, databases, and potentially many other infrastructure components. Each instance of a business transaction is an execution of that transaction in response to a particular user request (e.g., a socket call, illustratively associated with the TCP layer). A business transaction can be created by detecting incoming requests at an entry point and tracking the activity associated with request at the originating tier and across distributed components in the application environment (e.g., associating the business transaction with a 4-tuple of a source IP address, source port, destination IP address, and destination port). A flow map can be generated for a business transaction that shows the touch points for the business transaction in the application environment. In one embodiment, a specific tag may be added to packets by application specific agents for identifying business transactions (e.g., a custom header field attached to a hypertext transfer protocol (HTTP) payload by an application agent, or by a network agent when an application makes a remote socket call), such that packets can be examined by network agents to identify the business transaction identifier (ID) (e.g., a Globally Unique Identifier (GUID) or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focus on the performance of the services in the application environment from the perspective of end users. Performance monitoring based on business transactions can provide information on whether a service is available (e.g., users can log in, check out, or view their data), response times for users, and the cause of problems when the problems occur.

A business application is the top-level container in the application intelligence model. A business application contains a set of related services and business transactions. In some implementations, a single business application may be needed to model the environment. In some implementations, the application intelligence model of the application environment can be divided into several business applications. Business applications can be organized differently based on the specifics of the application environment. One consideration is to organize the business applications in a way that reflects work teams in a particular organization, since role-based access controls in the Controller UI are oriented by business application.

A node in the application intelligence model corresponds to a monitored server or JVM in the application environment. A node is the smallest unit of the modeled environment. In general, a node corresponds to an individual application server, JVM, or Common Language Runtime (CLR) on which a monitoring Agent is installed. Each node identifies itself in the application intelligence model. The Agent installed at the node is configured to specify the name of the node, tier, and business application under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the application intelligence model that includes one or more nodes. Each node represents an instrumented service (such as a web application). While a node can be a distinct application in the application environment, in the application intelligence model, a node is a member of a tier, which, along with possibly many other tiers, make up the overall logical business application.

Tiers can be organized in the application intelligence model depending on a mental model of the monitored application environment. For example, identical nodes can be grouped into a single tier (such as a cluster of redundant servers). In some implementations, any set of nodes, identical or not, can be grouped for the purpose of treating certain performance metrics as a unit into a single tier.

The traffic in a business application flows among tiers and can be visualized in a flow map using lines among tiers. In addition, the lines indicating the traffic flows among tiers can be annotated with performance metrics. In the application intelligence model, there may not be any interaction among nodes within a single tier. Also, in some implementations, an application agent node cannot belong to more than one tier. Similarly, a machine agent cannot belong to more than one tier. However, more than one machine agent can be installed on a machine.

A backend is a component that participates in the processing of a business transaction instance. A backend is not instrumented by an agent. A backend may be a web server, database, message queue, or other type of service. The agent recognizes calls to these backend services from instrumented code (called exit calls). When a service is not instrumented and cannot continue the transaction context of the call, the agent determines that the service is a backend component. The agent picks up the transaction context at the response at the backend and continues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailed transaction analysis for the leg of a transaction processed by the backend, the database, web service, or other application need to be instrumented.

The application intelligence platform uses both self-learned baselines and configurable thresholds to help identify application issues. A complex distributed application has a large number of performance metrics and each metric is important in one or more contexts. In such environments, it is difficult to determine the values or ranges that are normal for a particular metric; set meaningful thresholds on which to base and receive relevant alerts; and determine what is a “normal” metric when the application or infrastructure undergoes change. For these reasons, the disclosed application intelligence platform can perform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculates dynamic baselines for the monitored metrics, defining what is “normal” for each metric based on actual usage. The application intelligence platform uses these baselines to identify subsequent metrics whose values fall out of this normal range. Static thresholds that are tedious to set up and, in rapidly changing application environments, error-prone, are no longer needed.

The disclosed application intelligence platform can use configurable thresholds to maintain service level agreements (SLAs) and ensure optimum performance levels for system by detecting slow, very slow, and stalled transactions. Configurable thresholds provide a flexible way to associate the right business context with a slow request to isolate the root cause.

In addition, health rules can be set up with conditions that use the dynamically generated baselines to trigger alerts or initiate other types of remedial actions when performance problems are occurring or may be about to occur.

For example, dynamic baselines can be used to automatically establish what is considered normal behavior for a particular application. Policies and health rules can be used against baselines or other health indicators for a particular application to detect and troubleshoot problems before users are affected. Health rules can be used to define metric conditions to monitor, such as when the “average response time is four times slower than the baseline”. The health rules can be created and modified based on the monitored application environment.

Examples of health rules for testing business transaction performance can include business transaction response time and business transaction error rate. For example, health rule that tests whether the business transaction response time is much higher than normal can define a critical condition as the combination of an average response time greater than the default baseline by 3 standard deviations and a load greater than 50 calls per minute. In some implementations, this health rule can define a warning condition as the combination of an average response time greater than the default baseline by 2 standard deviations and a load greater than 100 calls per minute. In some implementations, the health rule that tests whether the business transaction error rate is much higher than normal can define a critical condition as the combination of an error rate greater than the default baseline by 3 standard deviations and an error rate greater than 10 errors per minute and a load greater than 50 calls per minute. In some implementations, this health rule can define a warning condition as the combination of an error rate greater than the default baseline by 2 standard deviations and an error rate greater than 5 errors per minute and a load greater than 50 calls per minute. These are non-exhaustive and non-limiting examples of health rules and other health rules can be defined as desired by the user.

Policies can be configured to trigger actions when a health rule is violated or when any event occurs. Triggered actions can include notifications, diagnostic actions, auto-scaling capacity, running remediation scripts.

Most of the metrics relate to the overall performance of the application or business transaction (e.g., load, average response time, error rate, etc.) or of the application server infrastructure (e.g., percentage CPU busy, percentage of memory used, etc.). The Metric Browser in the controller UI can be used to view all of the metrics that the agents report to the controller.

In addition, special metrics called information points can be created to report on how a given business (as opposed to a given application) is performing. For example, the performance of the total revenue for a certain product or set of products can be monitored. Also, information points can be used to report on how a given code is performing, for example how many times a specific method is called and how long it is taking to execute. Moreover, extensions that use the machine agent can be created to report user defined custom metrics. These custom metrics are base-lined and reported in the controller, just like the built-in metrics.

All metrics can be accessed programmatically using a Representational State Transfer (REST) API that returns either the JavaScript Object Notation (JSON) or the eXtensible Markup Language (XML) format. Also, the REST API can be used to query and manipulate the application environment.

Snapshots provide a detailed picture of a given application at a certain point in time. Snapshots usually include call graphs that allow that enables drilling down to the line of code that may be causing performance problems. The most common snapshots are transaction snapshots.

FIG. 4 illustrates an example application intelligence platform (system) 400 for performing one or more aspects of the techniques herein. The system 400 in FIG. 4 includes client device 405 and 492, mobile device 415, network 420, network server 425, application servers 430, 440, 450, and 460, asynchronous network machine 470, data stores 480 and 485, controller 490, and data collection server 495. The controller 490 can include visualization system 496 for providing displaying of the report generated for performing the field name recommendations for field extraction as disclosed in the present disclosure. In some implementations, the visualization system 496 can be implemented in a separate machine (e.g., a server) different from the one hosting the controller 490.

Client device 405 may include network browser 410 and be implemented as a computing device, such as for example a laptop, desktop, workstation, or some other computing device. Network browser 410 may be a client application for viewing content provided by an application server, such as application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed on network browser 410 and/or client 405 as a network browser add-on, downloading the application to the server, or in some other manner. Agent 412 may be executed to monitor network browser 410, the operating system of client 405, and any other application, API, or another component of client 405. Agent 412 may determine network browser navigation timing metrics, access browser cookies, monitor code, and transmit data to data collection 495, controller 490, or another device. Agent 412 may perform other operations related to monitoring a request or a network at client 405 as discussed herein including report generating.

Mobile device 415 is connected to network 420 and may be implemented as a portable device suitable for sending and receiving content over a network, such as for example a mobile phone, smart phone, tablet computer, or other portable device. Both client device 405 and mobile device 415 may include hardware and/or software configured to access a web service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419. Mobile device may also include client applications and other code that may be monitored by agent 419. Agent 419 may reside in and/or communicate with network browser 417, as well as communicate with other applications, an operating system, APIs and other hardware and software on mobile device 415. Agent 419 may have similar functionality as that described herein for agent 412 on client 405, and may report data to data collection server 495 and/or controller 490.

Network 420 may facilitate communication of data among different servers, devices and machines of system 400 (some connections shown with lines to network 420, some not shown). The network may be implemented as a private network, public network, intranet, the Internet, a cellular network, Wi-Fi network, VoIP network, or a combination of one or more of these networks. The network 420 may include one or more machines such as load balance machines and other machines.

Network server 425 is connected to network 420 and may receive and process requests received over network 420. Network server 425 may be implemented as one or more servers implementing a network service, and may be implemented on the same machine as application server 430 or one or more separate machines. When network 420 is the Internet, network server 425 may be implemented as a web server.

Application server 430 communicates with network server 425, application servers 440 and 450, and controller 490. Application server 450 may also communicate with other machines and devices (not illustrated in FIG. 4). Application server 430 may host an application or portions of a distributed application. The host application 432 may be in one of many platforms, such as including a Java, PHP, .Net, and Node.JS, be implemented as a Java virtual machine, or include some other host type. Application server 430 may also include one or more agents 434 (i.e., “modules”), including a language agent, machine agent, and network agent, and other software modules. Application server 430 may be implemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may be instrumented using byte code insertion, or byte code instrumentation (BCI), to modify the object code of the application or other software. The instrumented object code may include code used to detect calls received by application 432, calls sent by application 432, and communicate with agent 434 during execution of the application. BCI may also be used to monitor one or more sockets of the application and/or application server in order to monitor the socket and capture packets coming over the socket.

In some embodiments, server 430 may include applications and/or code other than a virtual machine. For example, servers 430, 440, 450, and 460 may each include Java code, .Net code, PHP code, Ruby code, C code, C++ or other binary code to implement applications and process requests received from a remote source. References to a virtual machine with respect to an application server are intended to be for exemplary purposes only.

Agents 434 on application server 430 may be installed, downloaded, embedded, or otherwise provided on application server 430. For example, agents 434 may be provided in server 430 by instrumentation of object code, downloading the agents to the server, or in some other manner. Agent 434 may be executed to monitor application server 430, monitor code running in a virtual machine 432 (or other program language, such as a PHP, .Net, or C program), machine resources, network layer data, and communicate with byte instrumented code on application server 430 and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents, such as language agents, machine agents, and network agents. A language agent may be a type of agent that is suitable to run on a particular host. Examples of language agents include a Java agent, .Net agent, PHP agent, and other agents. The machine agent may collect data from a particular machine on which it is installed. A network agent may capture network information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sending requests by application server 430, resource usage, and incoming packets. Agent 434 may receive data, process the data, for example by aggregating data into metrics, and transmit the data and/or metrics to controller 490. Agent 434 may perform other operations related to monitoring applications and application server 430 as discussed herein. For example, agent 434 may identify other applications, share business transaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier of nodes, or other entity. A node may be a software program or a hardware component (e.g., memory, processor, and so on). A tier of nodes may include a plurality of nodes which may process a similar business transaction, may be located on the same server, may be associated with each other in some other way, or may not be associated with each other.

A language agent may be an agent suitable to instrument or modify, collect data from, and reside on a host. The host may be a Java, PHP, .Net, Node.JS, or other type of platform. Language agents may collect flow data as well as data associated with the execution of a particular application. The language agent may instrument the lowest level of the application to gather the flow data. The flow data may indicate which tier is communicating with which tier and on which port. In some instances, the flow data collected from the language agent includes a source IP, a source port, a destination IP, and a destination port. The language agent may report the application data and call chain data to a controller. The language agent may report the collected flow data associated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host and collects network flow group data. The network flow group data may include a source IP, destination port, destination IP, and protocol information for network flow received by an application on which network agent is installed. The network agent may collect data by intercepting and performing packet capture on packets coming in from one or more network interfaces (e.g., so that data generated/received by all the applications using sockets can be intercepted). The network agent may receive flow data from a language agent that is associated with applications to be monitored. For flows in the flow group data that match flow data provided by the language agent, the network agent rolls up the flow data to determine metrics such as TCP throughput, TCP loss, latency, and bandwidth. The network agent may then report the metrics, flow group data, and call chain data to a controller. The network agent may also make system calls at an application server to determine system information, such as for example a host status check, a network status check, socket status, and other information.

A machine agent, which may be referred to as an infrastructure agent, may reside on the host and collect information regarding the machine which implements the host. A machine agent may collect and generate metrics from information such as processor usage, memory usage, and other hardware information.

Each of the language agent, network agent, and machine agent may report data to the controller. Controller 490 may be implemented as a remote server that communicates with agents located on one or more servers or machines. The controller may receive metrics, call chain data and other data, correlate the received data as part of a distributed transaction, and report the correlated data in the context of a distributed application implemented by one or more monitored applications and occurring over one or more monitored networks. The controller may provide reports, one or more user interfaces, and other information for a user.

Agent 434 may create a request identifier for a request received by server 430 (for example, a request received by a client 405 or 415 associated with a user or another source). The request identifier may be sent to client 405 or mobile device 415, whichever device sent the request. In embodiments, the request identifier may be created when data is collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an application and agents. Each application may run on the corresponding application server. Each of applications 442, 452, and 462 on application servers 440-460 may operate similarly to application 432 and perform at least a portion of a distributed business transaction. Agents 444, 454, and 464 may monitor applications 442-462, collect and process data at runtime, and communicate with controller 490. The applications 432, 442, 452, and 462 may communicate with each other as part of performing a distributed transaction. Each application may call any application or method of another virtual machine.

Asynchronous network machine 470 may engage in asynchronous communications with one or more application servers, such as application server 450 and 460. For example, application server 450 may transmit several calls or messages to an asynchronous network machine. Rather than communicate back to application server 450, the asynchronous network machine may process the messages and eventually provide a response, such as a processed message, to application server 460. Because there is no return message from the asynchronous network machine to application server 450, the communications among them are asynchronous.

Data stores 480 and 485 may each be accessed by application servers such as application server 460. Data store 485 may also be accessed by application server 450. Each of data stores 480 and 485 may store data, process data, and return queries received from an application server. Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of business transactions distributed over application servers 430-460. In some embodiments, controller 490 may receive application data, including data associated with monitoring client requests at client 405 and mobile device 415, from data collection server 495. In some embodiments, controller 490 may receive application monitoring data and network data from each of agents 412, 419, 434, 444, and 454 (also referred to herein as “application monitoring agents”). Controller 490 may associate portions of business transaction data, communicate with agents to configure collection of data, and provide performance data and reporting through an interface. The interface may be viewed as a web-based interface viewable by client device 492, which may be a mobile device, client device, or any other platform for viewing an interface provided by controller 490. In some embodiments, a client device 492 may directly communicate with controller 490 to view an interface for monitoring data.

Client device 492 may include any computing device, including a mobile device or a client computer such as a desktop, work station or other computing device. Client computer 492 may communicate with controller 490 to create and view a custom interface. In some embodiments, controller 490 provides an interface for creating and viewing the custom interface as a content page, e.g., a web page, which may be provided to and rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types of applications. Examples of applications that may implement applications 432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing the present technology, which is a specific implementation of device 200 of FIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts of the likes of clients 405, 492, network server 425, servers 430, 440, 450, 460, asynchronous network machine 470, and controller 490 of FIG. 4. (Note that the specifically configured system 500 of FIG. 5 and the customized device 200 of FIG. 2 are not meant to be mutually exclusive, and the techniques herein may be performed by any suitably configured computing device.)

The computing system 500 of FIG. 5 includes one or more processors 510 and memory 520. Main memory 520 stores, in part, instructions and data for execution by processor 510. Main memory 510 can store the executable code when in operation. The system 500 of FIG. 5 further includes a mass storage device 530, portable storage medium drive(s) 540, output devices 550, user input devices 560, a graphics display 570, and peripheral devices 580.

The components shown in FIG. 5 are depicted as being connected via a single bus 590. However, the components may be connected through one or more data transport means. For example, processor unit 510 and main memory 520 may be connected via a local microprocessor bus, and the mass storage device 530, peripheral device(s) 580, portable or remote storage device 540, and display system 570 may be connected via one or more input/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 510. Mass storage device 530 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 520.

Portable storage device 540 operates in conjunction with a portable non-volatile storage medium, such as a compact disk, digital video disk, magnetic disk, flash storage, etc. to input and output data and code to and from the computer system 500 of FIG. 5. The system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system 500 via the portable storage device 540.

Input devices 560 provide a portion of a user interface. Input devices 560 may include an alpha-numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. Additionally, the system 500 as shown in FIG. 5 includes output devices 550. Examples of suitable output devices include speakers, printers, network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or other suitable display device. Display system 570 receives textual and graphical information, and processes the information for output to the display device.

Peripherals 580 may include any type of computer support device to add additional functionality to the computer system. For example, peripheral device(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 can include a personal computer, hand held computing device, telephone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. Various operating systems can be used including Unix, Linux, Windows, Apple OS, and other suitable operating systems, including mobile versions.

When implementing a mobile device such as smart phone or tablet computer, the computer system 500 of FIG. 5 may include one or more antennas, radios, and other circuitry for communicating over wireless signals, such as for example communication using Wi-Fi, cellular, or other wireless signals.

—AI-Driven Capacity Forecasting and Planning for Microservices Apps—

As mentioned above, in today's highly dynamic micro services environments, it is difficult to ensure that there is sufficient resource capacity and redundancy for microservices apps to serve projected future demand with required availability (e.g., meeting service level agreements, or SLAs). That is, when running applications on a cloud fabric, selecting a level of capacity (i.e., a number of primary and/or backup resources allocated to the apps, such as memory, processors, network bandwidth, virtual machines, servers, etc.) is generally imprecise and often results in either insufficient resources, thus slowing down the actions using those apps (e.g., business transactions, web page apps, etc.), or else is overly wasteful, allowing pre-allocated resources to go unused. This is particularly true in dynamically changing computing environments, such as those that are based on user activity (e.g., more or less need at certain times of the day, days of the week, days of the year, etc., or also at unexpected times). As also mentioned above, attempting to manually plan for capacity for microservices apps is inadequate for such dynamic, high-growth environments (e.g., growing 2×, 5×, 10×, etc., in short amounts of time).

While there are several capacity planning and forecasting tools that were developed for datacenter era, monolithic applications, there is a large gap between planning for those types of applications and planning for modern, distributed, microservices applications that run on private and public clouds. Modern applications, in particular, are much more distributed, dynamic, and highly ephemeral, and conventional forecasting tools lack the concepts required to model them correctly.

The techniques herein, therefore, provide an automated mechanism that generates capacity forecasts purpose-built for microservices, based on specified constraints (e.g., SLAs), application performance data (e.g., business transaction performance data) and infrastructure performance data, demand forecast, resource costs, and user intent.

Operationally, and with reference to computing environment 600 of FIG. 6, an application-aware capacity engine 610 can take in a plurality of inputs 620 in order to determine a capacity model 630. For instance, as shown, service dependencies 621 between the microservices of the application (e.g., as extracted from business transaction and/or tier flow maps) are important, as they define the topology and interrelation between the microservices. For example, a business transaction map may include the general flow from client devices through inbound servers, load balancers, payment processing, identify verification, inventory tracking, and so on. In addition to dynamic determination of microservice dependencies (e.g., network connectivity determination, business transaction/tier tracing, etc.), manual inputs of the dependencies may also be established and utilized, such as taking a mapping from a network architecture design specification.

Additionally, the inputs 620 may also include one or specified constraints 622. For instance, the constraints 622 may comprise various user specified intent/constraints, and/or may be based on orchestrated policy configuration. As one example, application timing constraints may be specified, such as, e.g., application response time (ART), application delay, server response time, total response time, total transaction time, and so on. As other examples, various resource limitations may be specified, such as maximum processor utilization, memory utilization, etc. Any desired constraint or intent may be specified, including maximum numbers of servers, set numbers of resources (e.g., only having so many available servers in a private cloud, but being able to), maximum numbers of completed transactions within a given time frame, maximum cost spent on resources, and so on. Also, various levels of preference may be set as a constraint 622, including, for example, preferring to use less costly resources, more available resources (e.g., spinning up new servers may be easier than adding memory or processing capacity to a current server), or other preferences or weighted decision making.

According to the techniques herein, an existing service level/performance 623 may also be determined. Service level, as described herein, is any measurable metric of a service. For instance, a service level may be a number of logins per minute, a number of transactions per hour, a number of page loads per second, and so on. Though a single existing service level may be useful, in order to perform linear regression and curve fitting, at least two (and preferably more) data points should be examined. As such, a monitored period (e.g., values throughout a range of time or else at a plurality of distinct points in time) may be used to provide a collection of service levels. For example, at a first measured time (t0), 100 transactions per hour may be occurring, and at second and third measured times (t1, t2), 300 and 200 transactions per hour may be occurring, respectively.

Corresponding infrastructure (infra level) performance data 624 is also collected over the monitored period (e.g., a single point in time, or more generally, over a plurality of instances of time, such as a range or a plurality of discrete measurement points). For example, if the service level data 623 was captured at t0, t1, and t2, then the corresponding performance data 624 should be collected for those same data points. The number of data categories to collect for performance data 624 may vary based on implementation, and may be generally limited to those metrics that are adjustable (e.g., through allocation of more or fewer resources, or through changing settings on resources, etc.), or that effect or represent features that may be controllable (e.g., temperature).

In certain embodiments, inputs 620 may further comprise service demand forecast data 625, which may include dynamically determined data (e.g., based on trends, predictions, historical accounts of seasonality, etc.), or may be based on administrator-entered projections (e.g., 2×, 5×, 10×, etc.). Another optional input may include resource cost 626, which may include current costs, future costs, predicted costs, and so on, according to a current or future/predicted level of allocated resources and associated costs for such allocations.

According to the techniques herein, the application-aware capacity engine 610 processes the inputs 620 to detect patterns, extrapolate missing values, predict future values, and so on, in order to establish a resource capacity (or allocation) model 630, that may be based specifically on determining the minimum (or otherwise adequate) number of (or setting of) resources in the microservices architecture in order to support a given service level. For instance, in various embodiments, resource allocation process 248 may utilize machine learning techniques, to perform the model generation herein. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators), and recognize complex patterns in these data. Accordingly, by using machine learning techniques such as linear regression, curve fitting, and principle component analysis, the techniques herein may extrapolate and predict the specific resource capacity required to satisfy the constraints (e.g., SLAs) given the underlying network (i.e., microservices architecture) topology.

FIG. 7 illustrates an example resource capacity model 700 based on curves 710 of performance metrics 712 mapped against a service level 715. For instance, in the example above, assume that service levels of 100, 200, and 300 business transactions per hour were determined (notably at times t0, t3, and t2 above, though other than for trend prediction, may not be relevant to the curves 710). At those service levels, assume that various performance metrics 717 are also determined, such as total processor utilization (717 a), total memory utilization (717 b), total bandwidth through a load balancer (717 c), and so on. Other metrics, including those specifically related to the constraints or otherwise (e.g., application response time, etc.), may also be included, and the view shown herein is merely for sake of discussion. The engine 610 can extrapolate values between the measured data points (e.g., 150 and 250 transactions per hour), and can also extend beyond the outer limits of the measured data, particularly for trends into greater future values.

Note that a plurality of service levels may also be used as a reference at any given time (e.g., multi-dimensional solutions), and the view shown herein is merely a simplified example. For instance, service levels 715 may comprise business transactions per hour as one dimension, and number of simultaneous new user registrations in another dimension.

The resource capacity model 700 also includes the determined capacity 720, which may include any number of resource allocation suggestions. As shown, for example, a bandwidth allocation (e.g., reservation, open ports, different connections entirely, etc.) may be determined to correspond generally in terms of growth with the number of new transactions (e.g., increasing at substantially the same rate). It is important to recognize, however, that growth in service level does not necessarily imply a corresponding 1-to-1 resource necessity growth. For instance, another resource, such as memory utilization, may not grow at quite the same rate (that is, a 2× or 3× growth in business transactions does not result in a 2× or 3× growth in memory). Those skilled in the art will understand that although resources and metrics are not shown to decrease herein, this need not always be the case. For example, it is possible that certain performance metrics may go down over time or at certain times, including both for availability-type metrics (e.g., measuring remaining available CPU will go down as the service level goes up), but also for instances where an increase in the service level produces a decrease in performance metric/resource utilization (e.g., where additional or alternative services “kick in” after certain levels are reached, resulting in, for example, faster response times, less delay, and so on, until that new resource becomes saturated as well).

With this resource capacity model 700, the appropriate allocation of resources can be determined according to the embodiments herein. For instance, the techniques herein may optimize current systems, such as when it can be determined for a current level of service that there are insufficient resources (e.g., “choke points”) in certain parts of the architecture, such as inadequate load balancers, and/or an overage of resources, e.g., a plurality of virtual machines operating at less than 50% capacity. Also, for future assumptions or predictions of service levels, resources may be pre-allocated (an increase or decrease) or pre-provisioned accordingly. In particular, since the forecasting of microservices applications is a dynamic and ever-changing formulation (including growth or decline over time, seasonality, unexpected but identifiable resurgence of repeated patterns (e.g., viral video/shopping patterns)), the engine 610 can run updates to predictions over time (e.g., continuously, on-demand, or periodically), or may confirm that a current capacity is optimized at discreet points in the future (e.g., running capacity audits).

Notably, mentions herein for required capacity, allocated resources, minimum availability, and so on, may refer to an absolute value for service, or may include some degree of buffer, overage, or backup capacity. For instance, specified policies (or constraints) may require that no more than 90% maximum capacity of any given resource be reached. As another example, a policy may be set that a 20% overage buffer must be available to account for unexpected bursts. In still a further example, a primary and backup allocation may be defined, such as 1:1 coverage (e.g., 100% recovery of all resources, or certain resources), or else some percentage of coverage (e.g., 25% capacity in backup resources to cover whatever primary resources are currently allocated).

In accordance with one or more embodiments herein, the resource capacity engine 610 need not only determine the capacity model 630, but may also effectuate the allocation. That is, the model and forecast can be used in automate (or recommend) the advance purchase of public cloud reserved instances, right sizing a private cloud, etc. That is, the techniques herein may be used to result in a course of action being taken (e.g., directly by the engine 610 or else at the direction of the engine 610 through reports, alarms, alerts, and so on), such as to provision, automate, spin-up, purchase, alleviate, release resources as needed to satisfy any constraints.

Illustratively, assume, for example, that a business transaction ecosystem is configured within a microservices application architecture. Based on specific customer load, the system herein can determine associated infrastructure performance levels for that service level (i.e., that customer load). If customer demand is expected to increase 2× or 5× next year, or if an application optimization redesign is expected to reduce memory utilization by 50%, or other reasons, the techniques herein, using machine learning analysis, can help determine what resource demands need to be planned in advance, while still remaining within the specified constraints (e.g., service operation at less than 50% CPU utilization). For instance, by comparing the expected (or current) service level to the model 700, resource capacities 720 corresponding to that service level can be determined, and effectuated accordingly.

FIGS. 8A-8B illustrate an example simplified procedure for AI-driven capacity forecasting and planning for microservices apps in a network in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device 200) may perform procedure 800 by executing stored instructions (e.g., process 248). The procedure 800 may start at step 805, and continues to step 810, where, as described in greater detail above, the resource allocation process 248 determines service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications (e.g., business transactions) within a particular computer network topology. For example, as described above, the service dependency may be extracted from business transaction and tier flow maps.

In addition, in step 815, one or more specified performance metric constraints on operation of the plurality of applications may also be determined. For instance, performance metric constraints may be based on one or more service level agreements (SLAs), or one or more policy suite policies. Types of performance metric constraints may comprise such things are timing constraints (e.g., application response time (ART), application delay, server response time, total response time, total transaction time, etc.), resource limitations (e.g., maximum processor utilization, memory utilization, etc.). The metric constraints may be user-specified, or may be determined dynamically according to various policy engines.

According to the techniques herein, in step 820 the resource allocation process determines, from a monitored period (contemporaneously monitored or based on a historical account), a plurality of service levels of the plurality of applications. For example, over a period of time, various service levels may be determined based on any measurable unit, such as transactions per minute, page loads, bytes downloaded, purchases made, users logged on, and so on.

In addition, in step 825, during that monitored period, the resource allocation process also examines infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period. Specifically, the infrastructure performance data may comprise utilization of the plurality of resources as well as a plurality of performance metrics corresponding to each of the plurality of service levels. For instance, the techniques herein can determine that at a first service level, a first set of performance metrics exist, while at a second, third, or fourth service level, a respective second, third, and fourth set of performance metrics exist. The performance metrics of interest may generally be based on the performance metric constraints determined above, though may also be based on additional metrics for future or other usage.

As described above, each particular metric may result in a different response curve according to the service levels. As such, in step 830, the techniques herein may generate a resource capacity model for the microservices architecture, such as by extrapolating the data obtained above according to one or more machine learning techniques. That is, according to one or more embodiments herein, the resource capacity model may be based on the service dependency and the infrastructure performance data across the plurality of service levels, and defines a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels. Illustratively, generating the resource capacity model may be based on machine learning techniques such as linear regression, curve fitting, principle component analysis, and so on. Note also that required capacity of resources may comprise both primary resources and backup resources (e.g., a set amount of primary resources, plus an additional amount of backup for expected error in the calculation, determined variance in the metrics, policies regarding backup capabilities, and so on).

Optionally, in step 835, the techniques herein may determine, for a particular time in the future, a particular service level either according to a service demand forecast for operation of the plurality of applications at the particular time (e.g., forecast models for growth or “seasonality” or other time-basis for application operation variance), or else according to a user input (e.g., an expected 2×, 5×, 10× growth, and so on). Alternatively, as mentioned above, the particular time may simply be a current time, allowing for a determination to be made as to whether resources are currently properly allocated.

In step 840, the resource allocation process may then effectuate, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications (e.g., current, impending, or future) at a particular service level (i.e., determined or forecasted for that particular time). Effectuating, according to one or more of the embodiments herein, may comprise autonomously changing an allocation of resources for the microservices architecture, setting an allocation of public cloud resource instances, dictating a right-sizing of private cloud resources, generating alarms for administrator intervention, and other resource allocation measures or mitigations, accordingly. Note that the effectuating may involve both increasing and/or decreasing certain resources at any given time, including increasing some while decreasing others, and vice versa.

In certain embodiments, in step 845, the techniques herein may also determine, based on a resource cost model (e.g., the cost associated with allocation of each of the particular resources), a cost associated with effectuating the specific capacity of the plurality of resources required for the particular time of operation of the plurality of applications at the particular service level. For instance, once a set level of resources is defined for each of the plurality of resources, a total cost associated with the effectuated resources can be determined by aggregating per-resource costs of those resources, accordingly.

The procedure 800 may then end in step 850, notably with the ability to update forecasts, adjust the model, and allocate future resources, accordingly.

It should be noted that while certain steps within procedure 800 may be optional as described above, the steps shown in FIGS. 8A-8B are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein.

The techniques described herein, therefore, provide for AI-driven capacity forecasting and planning for microservices apps. In particular, as described above, the techniques herein provide dynamic performance metric-based forecasting. Specifically, using business transactions and associated service infrastructure performance metrics, the techniques herein develop a capacity model for a given microservices computing environment that allows making microservices app capacity predictions based on load profiles, service resource usage, and user-supplied constraints.

In still further embodiments of the techniques herein, a business impact of the capacity forecasting and planning for microservices apps can also be quantified. That is, because of issues related to specific applications/processes (e.g., lost traffic, slower servers, overloaded network links, etc.), various corresponding business transactions may have been correspondingly affected for those applications/processes (e.g., online purchases were delayed, page visits were halted before fully loading, user satisfaction or dwell time decreased, etc.), while other processes (e.g., on other network segments or at other times) remain unaffected. The techniques herein, therefore, can correlate the microservices apps capacity with various business transactions in order to better understand the affect that certain capacity metrics (e.g., SLA metrics based on capacity) may have had on the business transactions, accordingly.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the illustrative resource allocation process 248, which may include computer executable instructions executed by the processor 220 to perform functions relating to the techniques described herein, e.g., in conjunction with corresponding processes of other devices in the computer network as described herein (e.g., on network agents, controllers, computing devices, servers, etc.).

According to the embodiments herein, a method herein may comprise: determining, by a resource allocation process, service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determining, by the resource allocation process, one or more specified performance metric constraints on operation of the plurality of applications; determining, by the resource allocation process, during a monitored period, a plurality of service levels of the plurality of applications; examining, by the resource allocation process, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generating, by the resource allocation process, a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuating, by the resource allocation process based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications at a particular service level.

In one embodiment, the method further comprises determining, for the particular time being in the future, the particular service level according to a service demand forecast for operation of the plurality of applications at the particular time. In one embodiment, the method further comprises determining, for the particular time being in the future, the particular service level according to a user input. In one embodiment, the particular time is a current time. In one embodiment, the method further comprises determining, based on a resource cost model, a cost associated with effectuating the specific capacity of the plurality of resources required for the particular time of operation of the plurality of applications at the particular service level.

In one embodiment, effectuating comprises autonomously changing an allocation of resources for the microservices architecture. In one embodiment, generating the resource capacity model is based on one or more machine learning techniques selected from a group consisting of: linear regression; curve fitting; and principle component analysis. In one embodiment, effectuating comprises setting an allocation of public cloud resource instances. In one embodiment, effectuating comprises dictating a right-sizing of private cloud resources. In one embodiment, the required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications comprise both primary and backup resources. In one embodiment, determining the service dependency between the plurality of services in the microservices architecture comprises extracting the service dependency from business transaction and tier flow maps. In one embodiment, the one or more specified performance metric constraints on operation of the plurality of applications are based on one or both of one or more service level agreements (SLAs) or one or more policy suite policies. In one embodiment, the one or more specified performance metric constraints on operation of the plurality of applications are selected from a group consisting of: application response time (ART); application delay; server response time; total response time; and total transaction time. In one embodiment, the plurality of applications comprise a plurality of business transactions.

According to the embodiments herein, a tangible, non-transitory, computer-readable medium herein may have computer-executable instructions stored thereon that, when executed by a processor on a computer, may cause the computer to perform a method comprising: determining service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determining one or more specified performance metric constraints on operation of the plurality of applications; determining, during a monitored period, a plurality of service levels of the plurality of applications; examining, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generating a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuating, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications at a particular service level.

Further, according to the embodiments herein an apparatus herein may comprise: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process, when executed, configured to: determine service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determine one or more specified performance metric constraints on operation of the plurality of applications; determine, during a monitored period, a plurality of service levels of the plurality of applications; examine, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generate a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuate, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications at a particular service level.

While there have been shown and described illustrative embodiments above, it is to be understood that various other adaptations and modifications may be made within the scope of the embodiments herein. For example, while certain embodiments are described herein with respect to certain types of networks in particular, the techniques are not limited as such and may be used with any computer network, generally, in other embodiments. Moreover, while specific technologies, protocols, and associated devices have been shown, such as Java, TCP, IP, and so on, other suitable technologies, protocols, and associated devices may be used in accordance with the techniques described above. In addition, while certain devices are shown, and with certain functionality being performed on certain devices, other suitable devices and process locations may be used, accordingly. That is, the embodiments have been shown and described herein with relation to specific network configurations (orientations, topologies, protocols, terminology, processing locations, etc.). However, the embodiments in their broader sense are not as limited, and may, in fact, be used with other types of networks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. Certain features that are described in this document in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Further, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure are described in terms of being performed “by a server” or “by a controller”, those skilled in the art will appreciate that agents of the application intelligence platform (e.g., application agents, network agents, language agents, etc.) may be considered to be extensions of the server (or controller) operation, and as such, any process step performed “by a server” need not be limited to local processing on a specific server device, unless otherwise specifically noted as such. Furthermore, while certain aspects are described as being performed “by an agent” or by particular types of agents (e.g., application agents, network agents, etc.), the techniques may be generally applied to any suitable software/hardware configuration (libraries, modules, etc.) as part of an apparatus or otherwise.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Moreover, the separation of various system components in the embodiments described in the present disclosure should not be understood as requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true intent and scope of the embodiments herein. 

What is claimed is:
 1. A method, comprising: determining, by a resource allocation process, service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determining, by the resource allocation process, one or more specified performance metric constraints on operation of the plurality of applications; determining, by the resource allocation process, during a monitored period, a plurality of service levels of the plurality of applications; examining, by the resource allocation process, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generating, by the resource allocation process, a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuating, by the resource allocation process based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications in the future at a particular service level.
 2. The method as in claim 1, further comprising: determining, for the particular time being in the future, the particular service level according to a service demand forecast for operation of the plurality of applications at the particular time.
 3. The method as in claim 1, further comprising: determining, for the particular time being in the future, the particular service level according to a user input.
 4. The method as in claim 1, further comprising: determining, based on a resource cost model, a cost associated with effectuating the specific capacity of the plurality of resources required for the particular time of operation of the plurality of applications at the particular service level.
 5. The method as in claim 1, wherein effectuating comprises: autonomously changing an allocation of resources for the microservices architecture.
 6. The method as in claim 1, wherein generating the resource capacity model is based on one or more machine learning techniques selected from a group consisting of: linear regression; curve fitting; and principle component analysis.
 7. The method as in claim 1, wherein effectuating comprises: setting an allocation of public cloud resource instances.
 8. The method as in claim 1, wherein effectuating comprises: dictating a right-sizing of private cloud resources.
 9. The method as in claim 1, wherein the required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications comprise both primary and backup resources.
 10. The method as in claim 1, wherein determining the service dependency between the plurality of services in the microservices architecture comprises: extracting the service dependency from business transaction and tier flow maps.
 11. The method as in claim 1, wherein the one or more specified performance metric constraints on operation of the plurality of applications are based on one or both of one or more service level agreements (SLAs) or one or more policy suite policies.
 12. The method as in claim 1, wherein the one or more specified performance metric constraints on operation of the plurality of applications are selected from a group consisting of: application response time (ART); application delay; server response time; total response time; and total transaction time.
 13. The method as in claim 1, wherein the plurality of applications comprise a plurality of business transactions.
 14. A tangible, non-transitory, computer-readable medium storing program instructions that cause a computer to execute a process comprising: determining service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determining one or more specified performance metric constraints on operation of the plurality of applications; determining, during a monitored period, a plurality of service levels of the plurality of applications; examining, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generating a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuating, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications in the future at a particular service level.
 15. The computer-readable medium as in claim 14, wherein the process further comprises: determining, for the particular time being in the future, the particular service level according to a service demand forecast for operation of the plurality of applications at the particular time.
 16. The computer-readable medium as in claim 14, wherein the process further comprises: determining, based on a resource cost model, a cost associated with effectuating the specific capacity of the plurality of resources required for the particular time of operation of the plurality of applications at the particular service level.
 17. The computer-readable medium as in claim 14, wherein effectuating comprises: autonomously changing an allocation of resources for the microservices architecture.
 18. An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to: determine service dependency between a plurality of services in a microservices architecture that utilizes a plurality of resources to process a plurality of applications within a particular computer network topology; determine one or more specified performance metric constraints on operation of the plurality of applications; determine, during a monitored period, a plurality of service levels of the plurality of applications; examine, during the monitored period, infrastructure performance data of the plurality of services in the microservices architecture in relation to each of the plurality of service levels of the plurality of applications during the monitored period, the infrastructure performance data comprising utilization of the plurality of resources and a plurality of performance metrics corresponding to each of the plurality of service levels; generate a resource capacity model for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications within the particular computer network topology at given service levels; and effectuate, based on the resource capacity model, a specific capacity of the plurality of resources required for a particular time of operation of the plurality of applications in the future at a particular service level. 